Optimizing Project Workflows: The Transformative Power of OpenAir’s Project Center
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Exciting times in tech! AI startup Cognition has unveiled Devin, claimed as the world's first fully autonomous AI software engineer. Devin's capabilities stretch beyond just understanding coding; it can learn new technologies, build and deploy applications, and even train AI models. But here’s the burning question: Can AI like Devin replace human developers in the near future?I believe AI can serve as a co-programmer, enhancing our work rather than replacing us. The complexity and nuance of real-world software development require human ingenuity, creativity, and strategic thinking—qualities that AI like Devin is yet to master. Let's discuss this! How do you see AI evolving in the software development field? Can AI truly replace the human touch in tech, or will it remain an invaluable partner that streamlines our workload and tackles the monotonous tasks, allowing us to focus on more complex challenges? Share your thoughts and let’s demystify the future of AI and human collaboration in software development! #ai #softwaredevelopment #softwaredevelopment
AI startup Cognition has made a significant splash in the tech community with the unveiling of Devin, promoted as the world's first fully autonomous AI software engineer. This innovative development has garnered considerable attention, especially among those in the technology sector, including software developers, software architects, DevOps engineers, and IT managers engaged in digital transformation efforts across various industries such as fintech, healthtech, and e-commerce. Devin underwent rigorous testing via the SWE-bench coding benchmark, where it was tasked with addressing complex issues in diverse open source projects on GitHub. The outcomes of these tests were remarkable, showcasing Devin’s capabilities, which far surpass those of existing tools like GitHub Copilot in terms of autonomy and problem-solving abilities. What sets Devin apart is its demonstrated proficiency in learning and utilizing new technologies without prior exposure. This is particularly relevant to tech professionals who must continually adapt to rapidly evolving technological landscapes. Cognition has shared multiple videos illustrating Devin's competencies, including the ability to independently build and deploy applications, identify and rectify bugs in codebases, and even train and fine-tune AI models. Devin’s introduction marks a pivotal shift in AI’s role within software engineering, indicating a future where AI can autonomously tackle intricate software engineering tasks. This evolution is crucial for tech professionals, suggesting a landscape where AI not only assists but also leads in certain aspects of the development process. For individuals in the tech industry, particularly those involved in software development and IT operations, Devin represents the zenith of what AI can achieve in their field. It provides a vision of a future where AI handles complex engineering tasks, allowing human engineers to focus on strategic, innovative, and creative aspects of technology development and implementation. Devin’s launch by Cognition represents a significant milestone in AI and software engineering, showcasing an AI’s ability to autonomously solve real-world software challenges. This development heralds a new era in technology, where AI’s role is integral to the software development process, offering valuable insights and inspiration for professionals striving to remain at the cutting edge of digital transformation. https://www.devin.fm/ #ai
Devin
devin.fm
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Developing data science experiments, storing the curated features and models with MLflow is really awesome for productivity of the Machine Learning Workflow. Just compare this to the former engineering using local Jupyter Notebooks....
Getting started with your first ML project and achieving meaningful results is an enjoyable experience. However, reaching this stage often involves numerous trial-and-error attempts, and if you can't remember all your setup parameters, you might accidentally run the same experiment twice. This is where MLflow comes to your rescue by aiding your memory gaps. MLflow allows you to track all your experiments, including essential meta information such as the datasets used, setup parameters, and test scores. Additionally, MLflow can save your models, enabling you to register potential ones effectively. This proves to be immensely helpful, especially as the number of tests increases. In the early stages of a new ML project, your primary objective is to have a straightforward run without errors and to gauge the initial results, providing you with a sense of the project's potential. At this stage, you may not prioritize "clean code" or "good architecture." As the project shows promise and starts to grow, manually running everything becomes impractical. For example, envision having to run five independent tests, waiting for each one to finish before starting the next. To overcome this challenge, employing pipelines is a beneficial approach. By setting up pipelines, you can input your parameters and watch as the process runs automatically. This becomes an opportune moment to enhance your code's modularity. However, if the project demands automation due to its use in (pre-)production or a high priority on style, incorporating CI/CD pipelines and version control becomes crucial. This allows you to build various pipelines for different scenarios and seamlessly integrate them using push/pull mechanisms. While writing tests becomes essential, automating the testing process enables you to adopt the role of a skilled developer. Ultimately, your project should have a real impact and be operational in production. Regularly monitoring results and retraining models when data drift is detected further solidify your accomplishments. Additionally, the ability to handle streaming data adds an extra layer of sophistication to your work. [source: https://lnkd.in/e8mU8cM9]
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I think that converting a business system into code can be challenging. I've found that accurately reflecting all aspects of the system's concepts and processes can require many passes for various reasons, such as a lack of understanding of the what the system is intended to do or a misunderstanding of what the system does in actuality. The challenges that I've observed compound when stakeholders and end-users are taken into account; the code's output making sense to the development team is arguably the bare minimum for whether or not the algorithm is useful. I didn't realize how deep and nuanced some actualized systems can get when I only had to make tools that were at least marginally useful to only myself or a handful of people. It seems to me that the more eyes look over an algorithm, the more bugs, mistakes, and missteps are revealed. This is because each new pair of eyes tends to bring a unique experience or a specialized bit of knowledge that the development team might not have known about or might not have understood, or even appreciated, the relevance or impact of. I figure that these observations are good. Unlike the problem of letting too many specialists have a hand in the development of a solution, heavy communication from a variety of backgrounds regarding the usefulness of a tool that emulates a real-world system opens the doors to exploring what the tool does well, what the tool doesn't do well, and what the tool doesn't do at all. And once these three angles are well-established and well-understood, then the scope can be refined, the implementation can be improved, and the projects or processes that call upon the tool can be better executed. #algorithms #communication #conception #reality #projectmanagement #engineering
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I'm thrilled to announce the launch of OpsHub, Inc. Insights, a much needed quality gap intelligence tool - purpose-built for #DevOps teams. Insights optimizes Dev and #QA efforts by predicting defect-prone areas, minimizing costly rework, and accelerating release velocity. Going beyond simple #testautomation and subjective metrics, the tool empowers teams to focus on high-risk areas and minimize blind spots, leading to faster delivery of high-quality software, https://lnkd.in/esVwFm3e
OpsHub Launches Insights: Quality Gap Intelligence Tool for DevOps
https://www.opshub.com
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Getting started with your first ML project and achieving meaningful results is an enjoyable experience. However, reaching this stage often involves numerous trial-and-error attempts, and if you can't remember all your setup parameters, you might accidentally run the same experiment twice. This is where MLflow comes to your rescue by aiding your memory gaps. MLflow allows you to track all your experiments, including essential meta information such as the datasets used, setup parameters, and test scores. Additionally, MLflow can save your models, enabling you to register potential ones effectively. This proves to be immensely helpful, especially as the number of tests increases. In the early stages of a new ML project, your primary objective is to have a straightforward run without errors and to gauge the initial results, providing you with a sense of the project's potential. At this stage, you may not prioritize "clean code" or "good architecture." As the project shows promise and starts to grow, manually running everything becomes impractical. For example, envision having to run five independent tests, waiting for each one to finish before starting the next. To overcome this challenge, employing pipelines is a beneficial approach. By setting up pipelines, you can input your parameters and watch as the process runs automatically. This becomes an opportune moment to enhance your code's modularity. However, if the project demands automation due to its use in (pre-)production or a high priority on style, incorporating CI/CD pipelines and version control becomes crucial. This allows you to build various pipelines for different scenarios and seamlessly integrate them using push/pull mechanisms. While writing tests becomes essential, automating the testing process enables you to adopt the role of a skilled developer. Ultimately, your project should have a real impact and be operational in production. Regularly monitoring results and retraining models when data drift is detected further solidify your accomplishments. Additionally, the ability to handle streaming data adds an extra layer of sophistication to your work. [source: https://lnkd.in/e8mU8cM9]
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Check out the latest article from Ashwini Lalit, Senior Manager of Product Quality at NimbleWork. Discover the power of a data-driven approach to Software Development, with AI-powered recommendations and integrated value streams, NimbleWork fosters a culture of proactive decision-making. Learn how we're transforming quality engineering for exceptional results in every release. https://lnkd.in/gNRwhbzz #QualityEngineering #DataDriven #AI #SoftwareDevelopment #BDD #qualitydriven #artificialintelligence #softwareengineering
Leveraging Data-Driven Insights for Quality Driven Development
https://www.nimblework.com
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By leveraging the data collected from #DevOps pipelines and integrating it into value stream management #vsm practices, organizations can gain a comprehensive, data-driven understanding of their end-to-end value delivery process. Here are a few examples of the same that we have been practicing and have benefited us. #bdd #continuousimprovement #continuoustesting #continuousintegration #cleancode #agile #agiletesting #leanmanagement
Check out the latest article from Ashwini Lalit, Senior Manager of Product Quality at NimbleWork. Discover the power of a data-driven approach to Software Development, with AI-powered recommendations and integrated value streams, NimbleWork fosters a culture of proactive decision-making. Learn how we're transforming quality engineering for exceptional results in every release. https://lnkd.in/gNRwhbzz #QualityEngineering #DataDriven #AI #SoftwareDevelopment #BDD #qualitydriven #artificialintelligence #softwareengineering
Leveraging Data-Driven Insights for Quality Driven Development
https://www.nimblework.com
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#LLMOps: Production #PromptEngineering Patterns with Hamilton. What you send to your large language model (LLM) is quite important. Small variations and changes can have large impacts on outputs, so as your product evolves, the need to evolve your prompts will too. LLMs are also constantly being developed and released, and so as LLMs change, your prompts will also need to change. Therefore it’s important to set up an iteration pattern to operationalize how you “deploy” your prompts so you and your team can move efficiently, but also ensure that production issues are minimized, if not avoided. In this post, we’ll guide you through the best practices of managing prompts with Hamilton, an open source micro-orchestration framework, making analogies to MLOps patterns, and discussing trade-offs along the way. The high level takeaways of this post are still applicable even if you don’t use Hamilton. https://lnkd.in/gyJvdXG3
LLMOps: Production prompt engineering patterns with Hamilton
towardsdatascience.com
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Sharing this article I wrote for Ikigai's blog with some insights on Platform Engineering and how it impacts today's software projects. If you're interested in learning more about this subject, feel free to reach out!
Ikigai Digital | Why do you need a Platform Engineering team in your project?
ikigaidigital.io
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Keep your projects transparent and your team in the loop with an effective Changelog! 🌟 A well-maintained Changelog provides a clear history of enhancements, fixes, and updates, ensuring everyone knows the evolution of the project. 🚀 🔹 Added features expand capabilities 🛠️ 🔹 Chores maintain the project's health 🧹 🔹 Removed elements declutter the system 🗑️ 🔹 Fixed issues improve stability 🛠️ 🔹 Changed aspects reflect ongoing refinement 🔁 🔹 Tasks represent the project's progress 📈 🔹 Security updates fortify defenses 🛡️ 🔹 Deprecated items signal time for change 🚦 Stay ahead in the dynamic world of tech with a Changelog that tells your project's story. Keep innovating, keep updating, and keep sharing! ✨ #Changelog #ProjectManagement #Transparency #Innovation #TechUpdates #ContinuousImprovement #Teamwork #Development #AI #ML #BigData
Keep your projects transparent and your team in the loop with an effective Changelog! 🌟 A well-maintained Changelog provides a clear history of enhancements, fixes, and updates, ensuring everyone knows the evolution of the project. 🚀 🔹 Added features expand capabilities 🛠️ 🔹 Chores maintain the project's health 🧹 🔹 Removed elements declutter the system 🗑️ 🔹 Fixed issues improve stab...
dev.to
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